A maximum entropy classification scheme for phishing detection using parsimonious features
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Date
2021-06-13
Journal Title
Journal ISSN
Volume Title
Publisher
TELKOMNIKA Telecommunication, Computing, Electronics and Control
Abstract
Over the years, electronic mail (e-mail) has been the target of several
malicious attacks. Phishing is one of the most recognizable forms of
manipulation aimed at e-mail users and usually, employs social engineering
to trick innocent users into supplying sensitive information into an imposter
website. Attacks from phishing emails can result in the exposure of
confidential information, financial loss, data misuse, and others. This paper
presents the implementation of a maximum entropy (ME) classification
method for an efficient approach to the identification of phishing emails. Our
result showed that maximum entropy with parsimonious feature space gives
a better classification precision than both the Naïve Bayes and support vector
machine (SVM)
Description
Keywords
Classification, Classification, Phishing, Social engineering, Nigeria, Digital Development, ACE: Technology Enhanced Learning, ACETEL